October 29-November 2, 2017
New York
The premier machine learning conference

TRACK TOPICS:

Track 1: BUSINESS
Analytics strategy & operationalization
Track 2: TECH
Predictive modeling &
machine learning methods
Track 3 (Day 1): MARKETING
Marketing & market research analytics
Track 3 (Day 2): CASE STUDIES
Varied business applications

SESSION LEVELS:

All level tracks Blue circle sessions are for All Levels   Red triangle sessions are Expert/Practitioner Level

Agenda Overview – New York 2017
Pre-Conference Workshops: Sunday, October 29, 2017
Full-day Workshop
Big Data: Proven Methods You Need
to Extract Big Value

Vladimir Barash, Graphika
Morning Session Workshop
R Bootcamp: For Newcomers to R
Max Kuhn, RStudio
Full-day Workshop
R for Machine Learning:
A Hands-On Introduction

Max Kuhn, RStudio

DAY 1, Monday, October 30, 2017
(PAW Financial & PAW Healthcare run in parallel on this day - dual registration required)
Located in Hall 1E
Exhibit Hall Hours are Monday 8:00am - 7:00pm and Tuesday 8:00am - 3:30pm

8:00-8:45am Registration
Room: Hall 1E
Networking over Coffee
Room: Exhibit Hall
8:45-8:50am Conference Chair Welcome • Room: 1E09/1E10
Eric Siegel, Predictive Analytics World
8:50-9:40am
KEYNOTE • Room: 1E09/1E10
Analytics for the Job: Tips and Tricks for Success
Anne Robinson, Verizon Wireless
9:40-10:00am Diamond Sponsor Presentation • Room: 1E09/1E10
Machine Learning Automation: Large Scale Adoption of Predictive Analytics
Satadru Sengupta, DataRobot
10:00-10:30am Exhibits & Morning Coffee Break
Room: Exhibit Hall

Book Signing with Eric Siegel, author of The Power to Predict who Will Click, Buy, Lie, Or Die
  Track 1—BUSINESS: Analytics strategy & operationalization
Room: 1E11
Track 2—TECH: Predictive modeling & machine
learning methods

Room: 1E09/1E10
Track 3—MARKETING: Marketing & market research analytics
Room: 1E12
10:30-11:15am Crisis response; analytics management Hand-labeled training data Churn modeling
Lessons from:
NYC Mayor's Office
Quickly Building an Analytics Environment to Address a Public Health Crisis in NYC
All level tracks
Simon Rimmele, NYC Mayor's Office of Data Analytics
Case Study: Bloomberg L.P.
Crowd-Sourcing and Quality:
How To Get The Best Out of Hand-Tagged Training Data for Machine Learning Models
All level tracks
Leslie Barrett, Bloomberg L.P.
Case Study: Paychex
Retention Modeling in Uncertain Economic Times
All level tracks
Rob Rolleston, Paychex
11:20-11:40am Education and team building Time series modeling Churn modeling
Lessons from:
LinkedIn
The Sprint for Teaching Data Science: LinkedIn Learning, Analytics, and the New Era of Just-In-Time Skills Training All level tracks

Steve Weiss, LinkedIn
Time Series Prediction with Twitter: A Case Study of Crime in New York City
Anasse Bari, New York University
Aaron McKinstry, Courant Institute of Mathematical Sciences of New York University
Chuan-Heng Lin, Pienso
Gen Xiang, Trinnacle Capital Management
Case Study: Atlassian
Predicting Customer Churn from Product Usage at Atlassian

Jennifer Prendki, Atlassian (formerly Walmart)
11:40am-12:00pm Market research and analytics
Case Study: Verizon Wireless
Predicting Brand Love With Wireless Behaviors

Michael Gooch-Breault, Verizon Wireless
Jade Xi, Verizon Wireless
12:05-1:30pm Lunch
Room: Exhibit Hall
12:25-1:15pm
Lunch & Learn • Room: 1E09/1E10
How to start on Machine Learning and Predictive Analytics
Rajesh Shekhar, DataRobot
1:30-2:15pm
KEYNOTE • Room: 1E09/1E10
The Predictability Predicament: Your Model Overlooks the Real Target

Claudia Perlich, Dstillery
2:15-2:35pm Diamond Sponsor Presentation • Room: 1E09/1E10
Opportunity - Driven Enterprise: Turning Business On Its Head
Krishna Kallakuri, diwo
  Track 1—BUSINESS: Analytics strategy & operationalization
Room: 1E11
Track 2—TECH: Predictive modeling & machine
learning methods

Room: 1E09/1E10
Track 3—MARKETING: Marketing & market research analytics
Room: 1E12
2:40-3:00pm Analytics strategy Analytical methods Acquisition for academic enrollment
Lessons from:
The Clorox Company
Getting Started with Data Science Driven Insights, Execution and Innovation in the CPG Industry All level tracks

Payel Chowdhury, The Clorox Company
Machine Learning vs. Feature Engineering: What should the Focus be in Attempting to Predict Customer Behaviour
Richard Boire, Environics Analytics
Case Study: Becker College
Acquisition Funnel for Higher Education

Feyzi Bagirov, Becker College
3:05pm-3:25pm Uplift Modeling
Case Study: Telenor; US Bank
Uplift Modeling: Optimize for Influence and Persuade by the Numbers

Eric Siegel, Predictive Analytics World
3:25-3:55pm Exhibits & Afternoon Coffee Break
Room: Exhibit Hall

  Track 1—BUSINESS: Analytics strategy & operationalization
Room: 1E11
Track 2—TECH: Predictive modeling & machine
learning methods

Room: 1E09/1E10
Track 3—MARKETING: Marketing & market research analytics
Room: 1E12
3:55-4:40pm Analytics strategy Analytical methods Churn modeling; uplift modeling
Lessons from:
Prudential Financial
Value Creation Through Analytics Innovation
All level tracks
Wayne Huang, Prudential Financial
Case Study: Citigroup
A Modified Logistic Regression Approach Enhanced by New Interactions and Scaling Detections through Random Forests and GBM

Yulin Ning, Citigroup
Case Study: The Co-operators
Which Predictive Model Will Best Help Increase Retention?

Emilie Lavoie-Charland, The Co-operators
4:45-5:30pm Building Data Science Teams Forecasting; analytical methods Optimizing outreach; uplift modeling
Lessons from:
Comcast

Accelerating Data Science InnovationAll level tracks
Bob Bress, Comcast
Case Study: Micron Technology
Demand Forecasting with Machine Learning

Colin Ard, Micron Technology
Using Rapid Experiments and Uplift Modeling to Optimize Outreach at Scale
Daniel Porter, BlueLabs
5:30-7:00pm Networking Reception
Room: Exhibit Hall

Sponsored By:
7:00pm Dinner with Strangers
Meet at Registration

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DAY 2, Tuesday, October 31, 2017
(PAW Financial & PAW Healthcare run in parallel on this day - dual registration required)
Located in Hall 1E
Exhibit Hall Hours are Monday 8:00am - 7:00pm and Tuesday 8:00am - 3:30pm

8:00-8:35am Registration
Room: Hall 1E
Networking over Coffee
Room: Exhibit Hall
8:35-8:40am Conference Chair Welcome • Room: 1E09/1E10
Eric Siegel, Predictive Analytics World
8:40-9:25am Special Plenary Session • Room: 1E09/1E10
What to Optimize? The Heart of Every Analytics Problem
Dr. John Elder, Elder Research, Inc.
9:25-9:40am Plenary Session • Room: 1E09/1E10
Industry Trends: Highlights from the 2017 Data Miner Survey
Karl Rexer, Rexer Analytics
9:40-10:00am Diamond Sponsor Presentation • Room: 1E09/1E10
Move Beyond Basic Targeting and Accelerate Sales with Help from Machine Learning
Kelley Gazdak, Dun & Bradstreet
  Track 1—BUSINESS: Analytics strategy & operationalization
Room: 1E11
Track 2—TECH: Predictive modeling & machine
learning methods

Room: 1E09/1E10
Track 3—MORE CASE STUDIES: Varied business applications
Room: 1E12
  Getting it deployed Data quality Data storytelling
10:00-10:45am Lessons from:
Honeywell
Operationalizing Analytics:
The Critical Last Mile to Value
All level tracks
William Groves, Honeywell
Three Steps for Improving
Data Quality for Predictive Analytics

Tom Redman, Data Quality Solutions
The Limits of Surveys and the Power of Google Search Data
Seth Stephens-Davidowitz, Author, Everybody Lies and former Google data scientist
10:45-11:15am Exhibits & Morning Coffee Break
Room: Exhibit Hall


Book Signing with Seth Stephens-Davidowitz, author of Everybody Lies
and former Google data scientist



Book Signing with Eric Siegel, author of The Power to Predict who Will Click, Buy, Lie, Or Die
11:15-11:35am Workforce analytics Best practices Legal applications
Lessons from:
Intel
How Intel Wins the Right Marketplace Talent with Analytics
All level tracks
Hai Harari, Intel
Q&A: Ask Dean and
Karl Anything (about Best Practices)

Dean Abbott, SmarterHQ
Karl Rexer, Rexer Analytics
Legal Ease: Applications of Predictive Analytics in the Law Sandeep Gopalan, Deakin University
11:40am-12:00pm Industry-leading case studies
Customer Journey Analytics: Blazing Paths to Customer Success
Steven Ramirez, Beyone the Arc
12:00-1:10pm Lunch
Room: Exhibit Hall
12:10-1:00pm
Lunch & Learn
Room: 1E09/1E10
4D Today, 5D Tomorrow
Doug Howarth, MEE Inc
1:10-1:55pm
KEYNOTE
Room: 1E09/1E10
UPS' Road to Optimization

Jack Levis, UPS
1:55-2:15pm Diamond Sponsor Presentation • Room: 1E09/1E10
The Spooky Side of Predictive Analytics: Opaque Models
Mark Davenport, The Trade Desk
2:15-3:00pm Expert Panel • Room: 1E09/1E10
Women in Predictive Analytics: Opportunities and Challenges
Moderator: Anne Robinson, Verizon Wireless
Panelists:
Tracie Coker Kambies, Deloitte Consulting LLP
Julia Minkowski, Signifyd (formerly Fiserv)
Pallavi Yerramilli, The Trade Desk
3:00-3:30pm Exhibits & Afternoon Coffee Break
Room: Exhibit Hall
  Track 1—BUSINESS: Analytics strategy & operationalization
Room: 1E11
Track 2—TECH: Predictive modeling & machine
learning methods

Room: 1E09/1E10
Track 3—MORE CASE STUDIES: Varied business applications
Room: 1E12
Analytics management Model interpretation PA adoption in a new industry
3:30-3:50pm Lessons from:
Vanguard
AI: From Prototype to
Production
All level tracks
Wanda Wang, Vanguard
Case Study: SmarterHQ
When Model Interpretation Matters: Understanding Complex Predictive Models

Dean Abbott, SmarterHQ
Case Study: RightShip
Overcoming Challenges Implementing a Risk Model in the Maritime Industry

Bryan Guenther, RightShip
3:55-4:15pm Salesforce applications
Case Study: Runzheimer
Using Mileage Logs to Predict Successful Sales Behavior
All level tracks
Ron Cowan, Snowflake Data
4:15-5:00pm Model deployment Data policy Logistics analytics
Lessons from:
John Hancock

A Shiny Way to Operationalizing Analytics All level tracks
Vishwa Kolla, John Hancock
Regulating Opacity: Solving for the Conflict Between Laws and Analytics
Andrew Burt, Immuta
Case Study: Cargonexx
Leveraging Machine Learning Techniques for Realtime Pricing in B2B Truck Logistics

Alwin Haensel, HAMS

Post-Conference Workshops: Wednesday, November 1, 2017
Full-day Workshop
The Advanced Data Preparation Bootcamp: Whip your Data into Shape
Dean Abbott, Abbott Analytics
Full-day Workshop
The Best and the Worst of Predictive Analytics:
Machine Learning Methods and Common Data Science Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day Workshop
Spark on Hadoop for Machine Learning: Hands-On Lab
James Casaletto, MapR Technologies


Post-Conference Workshop: Thursday, November 2, 2017
Full-day Workshop
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics

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